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Should you be so unlucky as to wind up in the hospital with a drug-resistant bacterial infection, doctors will need to figure out which antimicrobial drug has the best chance of killing your particular pathogen. With antibiotic resistance on the rise—and predicted to kill 10 million people per year by 2050—it’s not always an easy choice.
It would help clinicians to be able to mine your superbug’s genome for DNA sequences that indicate susceptibility or resistance to antibiotics. As a step toward that goal, bioinformaticians are tapping artificial intelligence to identify the most relevant sequences. They’re making progress, thanks to databases stuffed with thousands of genomes from different strains of pathogenic bacteria, along with corresponding data on whether those strains were susceptible or resistant to dozens of antibiotics.
Some researchers are training machine learning algorithms to identify known drug resistance genes in new strains of a ...
